Fitting Jump Models

We describe a new framework for fitting jump models to a sequence of data. The key idea is to alternate between minimizing a loss function to fit multiple model parameters, and minimizing a discrete loss function to determine which set of model parameters is active at each data point. The framework is quite general and encompasses popular classes of models, such as hidden Markov models and piecewise affine models. The shape of the chosen loss functions to minimize determine the shape of the resulting jump model.Comment: Accepted for publication in Automatic

Fixed-order FIR approximation of linear systems from quantized input and output data

Abstract The problem of identifying a fixed-order FIR approximation of linear systems with unknown structure, assuming that both input and output measurements are subjected to quantization, is dealt with in this paper. A fixed-order FIR model providing the best approximation of the input-output relationship is sought by minimizing the worst-case distance between the output of the true system and the modeled output, for all possible values of the input and output data consistent with their quantized measurements. The considered problem is firstly formulated in terms of robust optimization. Then, two different algorithms to compute the optimum of the formulated problem by means of linear programming techniques are presented. The effectiveness of the proposed approach is illustrated by means of a simulation example

Influence of single-nucleotide polymorphisms on deferasirox Ctrough levels and effectiveness

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Differentiating present-day from ancient bones by vibrational spectroscopy upon acetic acid treatment

Acetic acid treatment for an accurate differentiation between ancient and recent human bones was assessed using Raman and FTIR-ATR spectroscopies. Each set of skeletal samples was analysed by these techniques, prior and after chemical washing, in order to determine the variations in bone´s chemical composition and crystallinity. Bone samples were collected from several independent sources: recent bones burned under controlled experimental conditions or cremated, and archaeological (XVII century and Iron Age). The effect of acetic acid, expected to impact mostly on carbonates, was clearly evidenced in the spectra of all samples, particularly in FTIR-ATR, mainly through the bands typical of A- and B-carbonates. Furthermore, as seen for crematoria and archaeological samples, acetic acid was found to remove contaminants such as calcium hydroxide. Overall, acetic acid treatment can be an effective method for removing carbonates (exogenous but possibly also endogenous) and external contaminants from bone. However, these effects are dependent on the skeletal conditions (e.g. post-mortem interval and burning settings). In addition, this chemical washing was shown to be insufficient for an unequivocal discrimination between recent and archaeological skeletal remains. Based on the measured IR indexes, only cremated bones could be clearly distinguished.info:eu-repo/semantics/publishedVersio

Minimal LPV state-space realization driven set-membership identification

Abstract-Set-membership identification algorithms have been recently proposed to derive linear parameter-varying (LPV) models in input-output form, under the assumption that both measurements of the output and the scheduling signals are affected by bounded noise. In order to use the identified models for controller synthesis, linear time-invariant (LTI) realization theory is usually applied to derive a statespace model whose matrices depend statically on the scheduling signals, as required by most of the LPV control synthesis techniques. Unfortunately, application of the LTI realization theory leads to an approximate state-space description of the original LPV input-output model. In order to limit the effect of the realization error, a new set-membership algorithm for identification of input/output LPV models is proposed in the paper. A suitable nonconvex optimization problem is formulated to select the model in the feasible set which minimizes a suitable measure of the state-space realization error. The solution of the identification problem is then derived by means of convex relaxation techniques

Clinical patterns of disease: From early systemic lupus erythematosus to late-onset disease

Systemic lupus erythematosus (SLE) is a complex disease with an insidious clinical presentation. In up to half of the cases, SLE onset is characterized by clinical and serological manifestations that, although specific, are insufficient to fulfill the classification criteria. This condition, called incomplete SLE, could be as challenging as the definite and classifiable SLE and requires to be treated according to the severity of clinical manifestations. In addition, an early SLE diagnosis and therapeutic intervention can positively influence the disease outcome, including remission rate and damage accrual. After diagnosis, the disease course is relapsing-remitting for most patients. Time in remission and cumulative glucocorticoid exposure are the most important factors for prognosis. Therefore, timely identification of SLE clinical patterns may help tailor the therapeutic intervention to the disease course. Late-onset SLE is rare but more often associated with delayed diagnosis and a higher incidence of comorbidities, including Sjogren's syndrome. This review focuses on the SLE disease course, providing actionable strategies for early diagnosis, an overview of the possible clinical patterns of SLE, and the clinical variation associated with the different age-at-onset SLE groups

Sim2Real Bilevel Adaptation for Object Surface Classification using Vision-Based Tactile Sensors

In this paper, we address the Sim2Real gap in the field of vision-based tactile sensors for classifying object surfaces. We train a Diffusion Model to bridge this gap using a relatively small dataset of real-world images randomly collected from unlabeled everyday objects via the DIGIT sensor. Subsequently, we employ a simulator to generate images by uniformly sampling the surface of objects from the YCB Model Set. These simulated images are then translated into the real domain using the Diffusion Model and automatically labeled to train a classifier. During this training, we further align features of the two domains using an adversarial procedure. Our evaluation is conducted on a dataset of tactile images obtained from a set of ten 3D printed YCB objects. The results reveal a total accuracy of 81.9%, a significant improvement compared to the 34.7% achieved by the classifier trained solely on simulated images. This demonstrates the effectiveness of our approach. We further validate our approach using the classifier on a 6D object pose estimation task from tactile data.Comment: 6 pages, submitted to ICRA 202

Cytomolecular Classification of Thyroid Nodules Using Fine-Needle Washes Aspiration Biopsies

Fine-needle aspiration biopsies (FNA) represent the gold standard to exclude the malignant nature of thyroid nodules. After cytomorphology, 20-30% of cases are deemed "indeterminate for malignancy" and undergo surgery. However, after thyroidectomy, 70-80% of these nodules are benign. The identification of tools for improving FNA's diagnostic performances is explored by matrix-assisted laser-desorption ionization mass spectrometry imaging (MALDI-MSI). A clinical study was conducted in order to build a classification model for the characterization of thyroid nodules on a large cohort of 240 samples, showing that MALDI-MSI can be effective in separating areas with benign/malignant cells. The model had optimal performances in the internal validation set (n = 70), with 100.0% (95% CI = 83.2-100.0%) sensitivity and 96.0% (95% CI = 86.3-99.5%) specificity. The external validation (n = 170) showed a specificity of 82.9% (95% CI = 74.3-89.5%) and a sensitivity of 43.1% (95% CI = 30.9-56.0%). The performance of the model was hampered in the presence of poor and/or noisy spectra. Consequently, restricting the evaluation to the subset of FNAs with adequate cellularity, sensitivity improved up to 76.5% (95% CI = 58.8-89.3). Results also suggest the putative role of MALDI-MSI in routine clinical triage, with a three levels diagnostic classification that accounts for an indeterminate gray zone of nodules requiring a strict follow-up